diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
index aa3d8e67a..cebb33da0 100644
--- a/docs/_templates/layout.html
+++ b/docs/_templates/layout.html
@@ -28,3 +28,16 @@
 }
   </style>
 {% endblock %}
+
+{% block menu %}
+<p class="caption">
+  <span class="caption-text">Getting Started</span>
+</p>
+
+<ul>
+  <li class="toctree-l1"><a href="{{ pathto('notes/installation') }}">Installation</a></li>
+  <li class="toctree-l1"><a href="{{ pathto('notes/overview') }}">Overview</a></li>
+</ul>
+
+{{super()}}
+{% endblock %}
\ No newline at end of file
diff --git a/docs/conf.py b/docs/conf.py
index 5e693445a..63189289c 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -62,6 +62,9 @@
 
 html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 html_theme = "sphinx_rtd_theme"
+html_theme_options = {
+    'collapse_navigation': True
+}
 
 # html_theme = 'alabaster'
 
diff --git a/docs/img/clock.gif b/docs/img/clock.gif
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diff --git a/docs/img/dash3d_viz.jpg b/docs/img/dash3d_viz.jpg
new file mode 100644
index 000000000..d4ad098f8
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diff --git a/docs/img/koala.jpg b/docs/img/koala.jpg
new file mode 100644
index 000000000..6ea1ea190
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diff --git a/docs/img/ov_viz.jpg b/docs/img/ov_viz.jpg
new file mode 100644
index 000000000..2c6836d34
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diff --git a/docs/index.rst b/docs/index.rst
index d62ccedb0..c6512bd55 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -7,12 +7,14 @@ NVIDIA Kaolin library provides a PyTorch API for working with a variety of 3D re
 
 Kaolin library is part of a larger suite of tools for 3D deep learning research. For example, `Omniverse Kaolin app <https://docs.omniverse.nvidia.com/app_kaolin/app_kaolin/overview.html>`_ will allow interactive visualization of 3D checkpoints. To find out more about the Kaolin ecosystem, visit the `NVIDIA Kaolin Dev Zone page <https://developer.nvidia.com/kaolin>`_.
 
+
 .. toctree::
    :titlesonly:
-   :caption: Getting Started:
+   :maxdepth: 1
+   :caption: Tutorials:
 
-   notes/installation
-   notes/overview
+   notes/diff_render
+   notes/checkpoints
 
 .. toctree::
    :titlesonly:
diff --git a/docs/notes/checkpoints.rst b/docs/notes/checkpoints.rst
new file mode 100644
index 000000000..4dd361166
--- /dev/null
+++ b/docs/notes/checkpoints.rst
@@ -0,0 +1,146 @@
+3D Checkpoints
+==============
+
+.. image:: ../img/koala.jpg
+
+Visualizing 3D inputs and outputs of your model during training is an
+essential diagnostic tool. Kaolin provides a :ref:`simple API to checkpoint<writing checkpoints>` **batches of meshes, pointclouds and voxelgrids**, as well as **colors and
+textures**, saving them in :ref:`the USD format<file format>`. These checkpoints can then be visualized locally using :ref:`Kaolin Omniverse App<ov app>` or by launching :ref:`Kaolin Dash3D<dash 3d>` on the commandline, allowing remote visualization through a web browser.
+
+.. _writing checkpoints:
+
+Writing Checkpoints:
+--------------------
+
+In a common scenario, model performance is visualized for a
+small evaluation batch. Bootstrap 3D checkpoints in your python training
+code by configuring a :class:`~kaolin.visualize.Timelapse` object::
+
+    import kaolin
+    timelapse = kaolin.visualize.Timelapse(viz_log_dir)
+
+The ``viz_log_dir`` is the directory where checkpoints will be saved. Timelapse will create files and subdirectories under this path, so providing
+a dedicated ``viz_log_dir`` separate from your other logs and configs will help keep things clean. The :class:`~kaolin.visualize.Timelapse` API supports point clouds,
+voxel grids and meshes, as well as colors and textures.
+
+Saving Fixed Data
+^^^^^^^^^^^^^^^^^
+
+To save any iteration-independent data,
+call ``timelapse`` before your training loop
+without providing an ``iteration`` parameter, e.g.::
+
+    timelapse.add_mesh_batch(category='ground_truth',
+                             faces_list=face_list,
+                             vertices_list=gt_vert_list)
+    timelapse.add_pointcloud_batch(category='input',
+                                   pointcloud_list=input_pt_clouds)
+The ``category`` identifies the meaning of the data. In this toy example,
+the model learns to turn the ``'input'`` pointcloud into the ``'output'`` mesh. Both the ``'ground_truth'`` mesh and the ``'input'`` pointcloud batches are only saved once for easy visual comparison.
+
+Saving Time-varying Data
+^^^^^^^^^^^^^^^^^^^^^^^^
+To checkpoint time-varying data during training, simply call :meth:`~kaolin.visualize.Timelapse.add_mesh_batch`, :meth:`~kaolin.visualize.Timelapse.add_pointcloud_batch` or :meth:`~kaolin.visualize.Timelapse.add_voxelgrid_batch`, for example::
+
+    if iteration % checkpoint_interval == 0:
+        timelapse.add_mesh_batch(category='output',
+                                 iteration=iteration,
+                                 faces_list=face_list,
+                                 vertices_list=out_vert_list)
+
+.. Tip::
+    For any data type, only time-varying data needs to be saved at every iteration. E.g., if your output mesh topology is fixed, only save ``faces_list`` once, and then call ``add_mesh_batch`` with only the predicted ``vertices_list``. This will cut down your checkpoint size.
+
+Saving Colors and Appearance
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+We are working on adding support for colors and semantic ids to
+point cloud and voxel grid checkpoints. Mesh API supports multiple time-varying materials
+by specifying a :class:`kaolin.io.PBRMaterial`. For an example
+of using materials, see
+`test_timelapse.py <https://github.com/NVIDIAGameWorks/kaolin/blob/master/tests/python/kaolin/visualize/test_timelapse.py>`_. 
+
+Sample Code
+^^^^^^^^^^^
+We provide a `script <https://github.com/NVIDIAGameWorks/kaolin/blob/master/examples/tutorial/visualize_main.py>`_ that writes mock checkpoints, which can be run as follows::
+
+    python examples/tutorial/visualize_main.py \
+        --test_objs=path/to/object1.obj,path/to/object2.obj \
+        --output_dir=path/to/logdir
+
+In addition, see :ref:`diff_render` tutorial.
+
+.. _file format:
+
+Understanding the File Format:
+------------------------------
+
+Kaolin :class:`~kaolin.visualize.Timelapse` writes checkpoints using Universal Scene Descriptor (USD) file format (`Documentation <https://graphics.pixar.com/usd/docs/index.html>`_), developed with wide support for use cases in visual effects, including time-varying data. This allows reducing redundancy in written
+data across time.
+
+After checkpointing with :class:`~kaolin.visualize.Timelapse`, the input ``viz_log_dir`` will contain
+a similar file structure::
+
+    ground_truth/mesh_0.usd
+    ground_truth/mesh_1.usd
+    ground_truth/mesh_...
+    ground_truth/textures
+    input/pointcloud_0.usd
+    input/pointcloud_1.usd
+    input/pointcloud_...
+    output/mesh_0.usd
+    output/mesh_1.usd
+    output/mesh_...
+    output/pointcloud_0.usd
+    output/pointcloud_1.usd
+    output/pointcloud_...
+    output/textures
+
+Here, the root folder names correspond to the ``category`` parameter
+provided to :class:`~kaolin.visualize.Timelapse` functions. Each element
+of the batch of every type is saved in its own numbered ``.usd`` file. Each USD file can be viewed on its
+own using any USD viewer, such as `NVIDIA Omniverse View <https://www.nvidia.com/en-us/omniverse/apps/view/>`_, or the whole log directory can be visualized
+using the tools below.
+
+.. Caution::
+    Timelapse is designed to only save one visualization batch for every category and type. Saving multiple batches without interleaving the data can be accomplished by creating custom categories.
+
+.. _ov app:
+
+Visualizing with Kaolin Omniverse App:
+--------------------------------------
+
+.. image:: ../img/ov_viz.jpg
+
+USD checkpoints can be visualized using a dedicated Omniverse Kaolin App `Training Visualizer <https://docs.omniverse.nvidia.com/app_kaolin/app_kaolin/user_manual.html#training-visualizer>`_.
+This extension provides full-featured support and high-fidelity rendering
+of all data types and materials that can be exported using :class:`~kaolin.visualize.Timelapse`, and allows creating custom visualization layouts and viewing meshes in multiple time-varying materials. `Download NVIDIA Omniverse <https://www.nvidia.com/en-us/omniverse/>`_ to get started!
+
+.. _dash 3d:
+
+Visualizing with Kaolin Dash3D:
+-------------------------------
+
+.. image:: ../img/dash3d_viz.jpg
+
+Omniverse app requires local access to a GPU and to the saved checkpoints, which is not always possible.
+We are also developing a lightweight ``kaolin-dash3d`` visualizer,
+which allows visualizing local and remote checkpoints without specialized
+hardware or applications. This tool is bundled with the latest
+builds as a command-line utility (**Important**: set ``KAOLIN_INSTALL_EXPERIMENTAL=1`` to install; see :ref:`installation`).
+
+To start Dash3D on the machine that stores the checkpoints, run::
+
+    kaolin-dash3d --logdir=$TIMELAPSE_DIR --port=8080
+
+The ``logdir`` is the directory :class:`kaolin.visualize.Timelapse` was configured with. This command will launch a web server that will stream
+geometry to web clients. To connect, simply visit ``http://ip.of.machine:8080`` (or `localhost:8080 <http://localhost:8080/>`_ if connecting locally or with ssh port forwarding).
+
+Try it now:
+^^^^^^^^^^^^^
+
+See Dash3D in action by running it on our test samples and visiting `localhost:8080 <http://localhost:8080/>`_::
+
+    kaolin-dash3d --logdir=$KAOLIN_ROOT/tests/samples/timelapse/notexture/ --port=8080
+
+.. Caution:: Dash3d is still an experimental feature under active development. It only supports **triangle meshes** and **pointclouds** and cannot yet visualize colors, ids or textures. The web client was tested the most on `Google Chrome <https://www.google.com/chrome/>`_. We welcome your early feedback on our `github <https://github.com/NVIDIAGameWorks/kaolin/issues>`_!
\ No newline at end of file
diff --git a/docs/notes/diff_render.rst b/docs/notes/diff_render.rst
new file mode 100644
index 000000000..362ccd879
--- /dev/null
+++ b/docs/notes/diff_render.rst
@@ -0,0 +1,13 @@
+.. _diff_render:
+
+Differentiable Rendering
+========================
+
+.. image:: ../img/clock.gif
+
+Differentiable rendering can be used to optimize the underlying 3D properties, like geometry and lighting, by backpropagating gradients from the loss in the image space. We provide an end-to-end tutorial for using the :mod:`kaolin.render.mesh` API in a Jupyter notebook:
+
+`examples/tutorial/dibr_tutorial.ipynb <https://github.com/NVIDIAGameWorks/kaolin/blob/master/examples/tutorial/dibr_tutorial.ipynb>`_
+
+In addition to the rendering API, the tutorial uses Omniverse Kaolin App `Data Generator <https://docs.omniverse.nvidia.com/app_kaolin/app_kaolin/user_manual.html#data-generator>`_ to create training data, :class:`kaolin.visualize.Timelapse` to write checkpoints, and
+Omniverse Kaolin App `Training Visualizer <https://docs.omniverse.nvidia.com/app_kaolin/app_kaolin/user_manual.html#training-visualizer>`_ to visualize them.
\ No newline at end of file
diff --git a/docs/notes/installation.rst b/docs/notes/installation.rst
index ea7c2c121..51656da31 100644
--- a/docs/notes/installation.rst
+++ b/docs/notes/installation.rst
@@ -1,3 +1,5 @@
+.. _installation:
+
 Installation
 ============
 
diff --git a/docs/notes/overview.rst b/docs/notes/overview.rst
index 07c5e8c4f..472685102 100644
--- a/docs/notes/overview.rst
+++ b/docs/notes/overview.rst
@@ -1,5 +1,7 @@
-Overview
-========
+.. _overview:
+
+API Overview and Reference
+==========================
 
 Kaolin library
 --------------